Browsing by Author "Lombard, Belinda"
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- ItemA catalytic model for SARS-CoV-2 reinfections : performing simulation-based validation and extending the model to include nth infections(Stellenbosch : Stellenbosch University, 2023-12) Lombard, Belinda; Van Schalkwyk, Cari; Pulliam, Juliet; Stellenbosch University. Faculty of Science. Dept. of Applied Mathematics.ENGLISH SUMMARY: Background: A global pandemic of COVID-19, caused by SARS-CoV-2, was declared in March 2020. Subsequently, studies have revealed a high seroprevalence of SARS-CoV-2 in both South African and global populations, along with instances of multiple reinfections. Among various models, a catalytic model has been developed for detecting population-level increases in risk of reinfection, following primary infection. This thesis aims to assess how potential biases from imperfect data observation processes affect the catalytic model’s ability to detect increases in reinfection risk. Furthermore, the thesis extends the catalytic model to detect increases in the risk of multiple reinfections. Methods: Simulation-based validation involved creating different reinfection scenarios representing real life data, which were then used in the model’s fitting and projection procedure. Observed reinfections were simulated using a time-series of primary infections, representative of South African data. Scenarios included considering both imperfect observation (with constant observation probability or a probability dependent on primary infection count) and mortality. The method’s ability to detect increases in the reinfection risk was measured by determining both the clusters of reinfections and the proportion of points that fell above the projection interval. Following simulation-based validation, the method was extended to detect population-level increases in the risk of 𝑛𝑡ℎ infections. This extended method was applied to observed third infections in South Africa, with an additional model parameter representing increased reinfections during the Omicron wave. Simulation-based validation was conducted on the extended method to assess its ability to detect increases of varying magnitudes in the risk of third infection. Results: During the simulation-based validation of the original catalytic model, model parameters converged in most scenarios. Failure to converge was mostly related to insufficient cases to properly inform the model parameters during the fitting procedure. Scenarios where the model parameters did not converge, or where the simulated data did not accurately fit the model, were excluded from interpretation. Introducing an increase in the reinfection risk resulted in successful detection of an increase (even with small increments), although with delayed timing under lower observed infection numbers. Mortality from first infections, unaccounted for in the model, did not impact the method’s ability to detect increases in the reinfection risk. The method demonstrated high specificity, reliably distinguishing true increases in the reinfection risk from noise. The catalytic model was extended to detect increases in the risk of 𝑛𝑡ℎ infections, and the extended method’s ability to detect increases in the risk of third infections was validated. The additional third infection hazard representing increased reinfection risk observed during the Omicron wave was successfully fitted to the data, and the method effectively detected increases in the risk of third infections. Conclusion: The findings highlight the need for sufficient infection data and the importance of convergence as a prerequisite for result interpretation. The extended model reliably detected increases in the risk of two or more reinfections and demonstrated robustness under different observation processes and increases in reinfection risk scenarios.